rfClassification: rf classification

In pRoloc: A unifying bioinformatics framework for spatial proteomics

Description

Classification using the random forest algorithm.

Usage

rfClassification(object, assessRes, scores = c("prediction", "all",
  "none"), mtry, fcol = "markers", ...)

Arguments

object	An instance of class "MSnSet".
assessRes	An instance of class "GenRegRes", as generated by rfOptimisation.
scores	One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.
mtry	If assessRes is missing, a mtry must be provided.
fcol	The feature meta-data containing marker definitions. Default is markers.
...	Additional parameters passed to randomForest from package randomForest.

Value

An instance of class "MSnSet" with rf and rf.scores feature variables storing the classification results and scores respectively.

Author(s)

Laurent Gatto

Examples

library(pRolocdata)
data(dunkley2006)
## reducing parameter search space and iterations 
params <- rfOptimisation(dunkley2006, mtry = c(2, 5, 10),  times = 3)
params
plot(params)
f1Count(params)
levelPlot(params)
getParams(params)
res <- rfClassification(dunkley2006, params)
getPredictions(res, fcol = "rf")
getPredictions(res, fcol = "rf", t = 0.75)
plot2D(res, fcol = "rf")

pRoloc documentation built on Nov. 8, 2020, 6:26 p.m.